To obtain labeled video data is one of the most critical bottlenecks of exercising deep learning on videos.
This motivates the development of self-supervised learning to take out the burden of annotating the videos.
Mainstream self-supervised learning approaches [1]}, [2]}, [3]}, [4]}, [5]} learn pixel or instance representation for downstream video tasks, , object tracking, by exploiting temporal correspondence in unlabeled videos.
As a self-supervision signal, cycle consistency loss is often used
to train the tracking embedding.
By doing so, we can finetune SSIS-Track on unlabeled
videos and further enhance SSIS-Track's performance in paired tracking.
Beyond the existing methods, the tracking embedding
for the correspondence module in SSIS-Track is formed by,
paired shadow and object embeddings
where both space and time correspondences are formulated during the training to promote paired shadow-object tracking.
